1 citations · 2 across the 9 of their papers we have counts for
5 papers · 1 filter
Hierarchical and Permutation-Invariant Feature Transformation Learning via Policy-Guided Embedding Search
Rui Liu, Tao Zhe, Yanyong Huang +5
Feature transformation improves predictive performance on tabular data by constructing informative abstractions from raw features. Recent generative approaches encode transformatio…
Neural Structure Embedding for Symbolic Regression via Continuous Structure Search and Coefficient Optimization
Fateme Memar, Tao Zhe, Dongjie Wang
Symbolic regression aims to discover human-interpretable equations that explain observational data. However, existing approaches rely heavily on discrete structure search (e.g., ge…
Heterogeneous Multi-Agent Reinforcement Learning with Attention for Cooperative and Scalable Feature Transformation
Tao Zhe, Huazhen Fang, Kunpeng Liu +3
Feature transformation enhances downstream task performance by generating informative features through mathematical feature crossing. Despite the advancements in deep learning, fea…
Permutation-Invariant Representation Learning for Robust and Privacy-Preserving Feature Selection
Rui Liu, Tao Zhe, Yanjie Fu +3
Feature selection eliminates redundancy among features to improve downstream task performance while reducing computational overhead. Existing methods often struggle to capture intr…
Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation
Dongjie Wang, Yanyong Huang, Wangyang Ying +11
Tabular data is one of the most widely used formats across industries, driving critical applications in areas such as finance, healthcare, and marketing. In the era of data-centric…